The cassava starch market is promising in sub-Saharan Africa and increasing rapidly due to the numerous uses of starch in food industries. More accurate, high-throughput, and cost-effective phenotyping approaches could hasten the development of cassava varieties with high starch content to meet the growing market demand. This study investigated the effectiveness of a pocket-sized SCiO™ molecular sensor (SCiO) (740−1070 nm) to predict starch content in freshly ground cassava roots. A set of 344 unique genotypes from 11 field trials were evaluated. The predictive ability of individual trials was compared using partial least squares regression (PLSR). The 11 trials were aggregated to capture more variability, and the performance of the combined data was evaluated using two additional algorithms, random forest (RF) and support vector machine (SVM). The effect of pretreatment on model performance was examined. The predictive ability of SCiO was compared to that of two commercially available near-infrared (NIR) spectrometers, the portable ASD QualitySpec^® Trek (QST) (350−2500 nm) and the benchtop FOSS XDS Rapid Content™ Analyzer (BT) (400−2490 nm). The heritability of NIR spectra was investigated, and important spectral wavelengths were identified. Model performance varied across trials and was related to the amount of genetic diversity captured in the trial. Regardless of the chemometric approach, a satisfactory and consistent estimate of starch content was obtained across pretreatments with the SCiO (correlation between the predicted and the observed test set, (R²_P): 0.84−0.90; ratio of performance deviation (RPD): 2.49−3.11, ratio of performance to interquartile distance (RPIQ): 3.24−4.08, concordance correlation coefficient (CCC): 0.91−0.94). While PLSR and SVM showed comparable prediction abilities, the RF model yielded the lowest performance. The heritability of the 331 NIRS spectra varied across trials and spectral regions but was highest (H² > 0.5) between 871−1070 nm in most trials. Important wavelengths corresponding to absorption bands associated with starch and water were identified from 815 to 980 nm. Despite its limited spectral range, SCiO provided satisfactory prediction, as did BT, whereas QST showed less optimal calibration models. The SCiO spectrometer may be a cost-effective solution for phenotyping the starch content of fresh roots in resource-limited cassava breeding programs.

Cassava mosaic disease (CMD) is a major viral disease adversely affecting cassava production in Africa and Asia. Genomic regions conferring resistance to the disease have been mapped in African cassava germplasm through biparental quantitative trait loci (QTL) mapping and genome-wide association studies. To facilitate the utilization of these markers in breeding pipelines to support selections, proof-of-concept technical and biological validation research was carried out using independent pre-breeding and breeding populations. Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) assays were designed from three single nucleotide polymorphism (SNP) markers linked to a major resistance locus on chromosome 12 (S12_7926132, S12_7926163) and a minor locus on chromosome 14 (S14_4626854). The designed assays were robust and easy to score with >99% genotype call rate. The overall predictive accuracy (proportion of true positives and true negatives) of the markers (S12_7926132 and S14_4626854) was 0.80 and 0.78 in the pre-breeding and breeding population, respectively. On average, genotypes that carried at least one copy of the resistant allele at the major CMD2 locus had a significantly higher yield advantage. Nevertheless, variation was observed in prediction accuracies for the major locus (S12_7926132) among sub-families from the two populations, suggesting the need for context-specific utilization, for example, by screening for co-segregation of favorable SNP alleles with resistance in the parents being used for crosses. Availability of these validated SNP markers on the uniplex KASP genotyping platform represents an important step in translational genetics toward marker-assisted selection to accelerate introgression of favorable resistant alleles in breeding populations.

A validated marker system is crucial to running an effective genomics-assisted breeding program. We used 36 Kompetitive Allele-Specific PCR (KASP) markers to genotype 376 clones from the biofortified cassava pipeline, and fingerprinted 93 of these clones with DArTseq markers to characterize breeding materials and evaluate their relationships. The discriminating ability of the 36-quality control (QC) KASP and 6602 DArTseq markers was assessed using 92 clones genotyped in both assays. In addition, trait-specific markers were used to determine the presence or absence of target genomic regions. Hierarchical clustering identified two major groups, and the clusters were consistent with the breeding program origins. There was moderate genetic differentiation and a low degree of variation between the identified groups. The general structure of the population was similar using both assays. Nevertheless, KASP markers had poor resolution when it came to differentiating the genotypes by seed sources and overestimated the prevalence of duplicates. The trait-linked markers did not achieve optimal performance as all markers displayed variable levels of false positive and/or false negative. These findings represent the initial step in the application of genomics-assisted breeding for the biofortified cassava pipeline, and will guide the use of genomic selection in the future.

The response to the diverse needs along the cassava value chain, the urge to increase genetic gain, and the need for rapid varietal turnover will necessitate not only technological innovations but also transformation of public breeding programs in sub-Saharan Africa (SSA). We developed guiding, flexible and adaptative tools for portfolio management of cassava breeding. The cassava breeding and product development pipeline process was mapped to illustrate activities of each stage, as well as to clarify key decision points. Stakeholders involved at all stages of breeding were identified. This allowed for identification of gaps and new crucial functions. To clarify accountability and reduce complexity in the decision-making at key decision points, the roles were mapped against decision-rights at each stage-gate. Cassava crop calendars for the different regions in SSA were developed to facilitate better planning. A product advancement template was developed to guide product advancement. The tools that have been developed and stage-gate mapping, will support regional efforts to establish more structured, transparent, participatory, efficient, inclusive, and demand-driven cassava breeding in the region. These approaches could be customized to other commodities.

Cassava ( Manihot esculenta Crantz) is an important food security crop in sub-Saharan Africa and other tropical regions, but its genetic improvement is hindered by long breeding cycles and labour-intensive phenotyping procedures. This study aimed to develop a rapid phenotyping protocol and assess its predictive capacity for yield and plant architecture traits in cassava using Normalized Difference Vegetation Index (NDVI) data obtained with affordable handheld sensor (Trimble GreenSeeker). A diverse panel of 453 cassava accessions was evaluated across two contrasting agroecological zones in Nigeria; Mokwa (Southern Guinea Savannah) and Onne (Humid Forest) during the 2021/2022 planting season. NDVI data collected at 3, 6, and 9 months after planting (MAP) were integrated with ground truth phenotypic measurements of 26 agronomic traits.Genetic parameters including broad-sense heritability and genotype-by-environment interactions were estimated. Results showed moderate to high heritability for important traits such as fresh root yield (FYLD), dry matter content (DM), and harvest index (HI). NDVI data, especially at 6 months after planting, demonstrated strong predictive power (R² up to 0.9) for yield components, with prediction accuracy varying across locations. Significant negative correlations between lodging (LODG) and yield traits highlighted the influence of plant architecture on productivity in cassava. These findings affirm the applicability of handheld NDVI sensors as cost-effective tools for enhanced phenotyping and selection in cassava breeding programs for rapid genetic gains and varietal development under diverse field conditions.